Ai-based vehicle support system and method for use therewith

ABSTRACT

A vehicle transaction support system operates by: generating, based on customer prequalification data and extended bureau data and utilizing a customer grading engine having a first artificial intelligence model trained via machine learning, a customer grade that predicts a likelihood of transaction default associated with a customer; generating, based on customer profile data that includes the customer grade and other customer profile data and utilizing a vehicle recommendation engine having a second artificial intelligence model trained via machine learning, vehicle options data that indicates vehicles selected from a vehicle database; and generating, based on at least a portion of the customer profile data and the vehicle options data and utilizing a transaction recommendation engine having a third artificial intelligence model trained via machine learning, terms data associated with each of the vehicles selected from the vehicle database.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

INCORPORATION-BY-REFERENCE OF MATERIAL SUBMITTED ON A COMPACT DISC

Not applicable.

BACKGROUND Technical Field

This invention relates generally to artificial intelligence-basedprocessing systems used in conjunction with client/server and othernetwork architectures for supporting vehicle transactions.

Description of Related Art

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWING(S)

FIG. 1 is a schematic block diagram of an embodiment of a vehicletransaction support system;

FIG. 2A is a schematic block diagram of a mobile device in accordancewith various embodiments;

FIG. 2B is a schematic block diagram of one or more support subsystemsin accordance with various embodiments;

FIG. 3A is a schematic block diagram of a vehicle transaction supportsystem in accordance with various embodiments;

FIG. 3B is a flow diagram in accordance with various embodiments;

FIG. 3C is a graphic diagram of predicted cumulative likelihood oftransaction default in accordance with various embodiments;

FIG. 3D is a schematic block diagram of a customer grading engine inaccordance with various embodiments;

FIG. 3E is a schematic block diagram of a customer recommendation enginein accordance with various embodiments;

FIG. 3F is a schematic block diagram of a transaction recommendationengine in accordance with various embodiments;

FIG. 3G is a schematic block diagram of a terms selection model inaccordance with various embodiments;

FIG. 3H is a schematic block diagram of a vehicle transaction supportsystem in accordance with various embodiments;

FIG. 3I is a schematic block diagram of a transaction adjustment enginein accordance with various embodiments;

FIG. 3J is a schematic block diagram of a vehicle transaction supportsystem in accordance with various embodiments;

FIGS. 4A-4E are screen displays of an interactive interface inaccordance with various embodiments;

FIGS. 5A-5F are screen displays of an interactive interface inaccordance with various embodiments;

FIGS. 6A-6B are portions of screen displays of an interactive interfacein accordance with various embodiments;

FIGS. 7A-7D are flowchart representations of methods in accordance withvarious embodiments.

DETAILED DESCRIPTION

One or more embodiments are now described with reference to thedrawings, wherein like reference numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous details are set forth in order to provide athorough understanding of the various embodiments. It is evident,however, that the various embodiments can be practiced without thesedetails and without applying to any particular networked environment,computer architecture, processing system or standard.

FIG. 1 presents a vehicle transaction support system 100, which caninclude one or more support subsystems 101 that communicatebidirectionally with one or more mobile devices 120 via a wired and/orwireless network 150. The support subsystems 101 can include, forexample, a prequalification subsystem 102, a customer grading subsystem104, a vehicle selection subsystem 106, a transaction recommendationsubsystem 108, a terms adjustment subsystem 110, an agent subsystem 112and/or other support subsystems for providing automated vehicletransaction support to one or more customers, agents or other users.Some or all of the subsystems 101 can utilize the same processingdevices, memory devices, and/or network interfaces, for example, runningon a same server or other computing platform, set of cloud servers orother shared servers connected to network 150. Alternatively, or inaddition, some or all of the subsystems 101 can be assigned their ownprocessing devices, memory devices, and/or network interfaces, forexample, running separately on different sets of servers connected tonetwork 150. Some or all of the subsystems 101 can interact directlywith each other, for example, where one subsystem's output istransmitted directly as input to another subsystem via a networkconnection. Network 150 can include one or more wireless, optical and/orwired communication systems; one or more non-public intranet systemsand/or public Internet systems; and/or one or more local area networks(LAN) and/or wide area networks (WAN).

The vehicle transaction support system 100 can further include one ormore vehicle databases 115 corresponding to, for example, one or morepartner companies. Each vehicle database 115 can store one or moreshared databases and/or one or more files stored on one or more memorydevices that include vehicle information in the form of images, datafields, text, other media and/or other database entries as describedherein. The shared databases and/or files can each be utilized by someor all of the subsystems of the vehicle transaction support system 100,allowing some or all of the subsystems 101 and/or mobile devices 120 toretrieve, review and/or process entries from the one or more vehicledatabases 115.

One or more mobile devices 120 can each be associated with one or moreagents or users of one or more subsystems 101 of the vehicle transactionsupport system 100. Some of the mobile devices 120 allowing agents,customers and/or other users to operate or otherwise operate thefunctions and features of one or more subsystems 101 for which they areauthorized to access.

Some or all of the subsystems 101 of the vehicle transaction supportsystem 100 can include a server that presents a website for operationvia a browser of mobile devices 120. Alternatively or in addition, eachmobile device 120 can store specific application data, a correspondingdatabase and/or other data in a memory, data corresponding to some orall subsystems 101, for example, a subset of the subsystems 101 that arerelevant to the user of the mobile device 120. A processor of the mobiledevice 120 can operate via a display device to display an interactiveinterface, such as a graphical user interface, based on instructions inthe data stored in memory and/or received via the network 150. Forexample, a website implemented by a subsystem 101 can operate via theapplication. Some or all of the websites presented can correspond tomultiple subsystems 101, for example, where the multiple subsystemsshare the server presenting the website.

Each subsystem 101 can be configured separately or on a combined basiswith other subsystems 101 for user authentication such as two factorauthentication, password authentication or other authentication toprohibit unauthorized access. In addition, the support subsystems 101,vehicle databases 115 and mobile device 120 can employ encryption, suchas AES256 and/or other encryption algorithm to protect the security ofdata stored therein. Furthermore, the network 150 can be configured forsecure communications between the support subsystems 101, the mobiledevices 120 and the vehicle databases 115 to protect the security ofdata communicated between the support subsystems 101, the mobile devices120 and the vehicle databases 115.

In general, some or all of these functions discussed herein cannot bepractically be trained by the human mind. Instead, such functions and/orcorresponding models can be trained based on applying artificialintelligence algorithms and/or techniques, and/or machine learningalgorithms and/or techniques, to a training set of feature vectorsand/or document files. Alternatively or in addition, training some orall medical scan analysis functions cannot be practically be trained bythe human mind based upon: a great complexity of the resulting functionand/or corresponding model; a large size of the training set utilized totrain the function and/or model; the model taking an infeasibly longamount of time to train utilizing only pen and paper; and/or otherreasons. Some or all of these functions discussed herein cannot bepractically be performed by the human mind. Instead, such functions canbe performed utilizing artificial intelligence algorithms and/ortechniques, and/or machine learning algorithms and/or techniques.Alternatively or in addition, such functions can be performed utilizinga model trained utilizing artificial intelligence algorithms and/ortechniques, and/or machine learning algorithms and/or techniques.Alternatively or in addition, performing some or all functions cannot bepractically be performed by the human mind based upon: a greatcomplexity of these function; an accuracy rate and/or consistency ofgenerating output being more favorable than that of a human; a speed ofgenerating output being more favorable than that of a human; thesefunctions taking an infeasibly long amount of time to perform utilizingonly pen and paper; and/or other reasons.

In operation, the vehicle transaction support system 100 operates inconjunction with mobile devices 120 such as tablets or mobile phones toguide agents, customers and/or other users. The vehicle transactionsupport system 100 utilizes an artificial intelligence (AI) basedgrading process that is trained based on vast amounts of datarepresenting the results of tens of thousands (or more) prior vehicletransactions. The AI model operates based on dozens of inputs (or more)from customers to produce a unique customer profile and customerprequalification data that can be used to produce a “Grade” for acustomer that is different from a traditional, general and commerciallyavailable credit score. This customer grade, for example, can be used tosupplement a traditional credit score and/or in place of a traditionalcredit score in circumstances where a customer lacks such a score—todetermine a more accurate likelihood of transaction default. The vehicletransaction support system 100 improves the technology of automatedcredit scoring using AI models that improve the speed, consistency andaccuracy of grade calculations. Furthermore, the human mind is notequipped to operate or train such AI models given the vast volume oftraining data and the machine, i.e., non-human learning required toperform such operation and training.

In various embodiments, the vehicle transaction support system 100utilizes further AI models that are trained on vast training data setsrepresenting the results of tens of thousands (or more) prior vehicletransactions to recommend vehicle selections to a customer and furtherto determine a customer's eligibility for loan value, term, interestrate and down payment, etc. In this fashion, the AI models can supportoperates where vehicles and terms can be matched to a customer's needsand particular circumstances. The vehicle transaction support system 100improves the technology of automated vehicle and transaction termselection using AI models that improve the speed and accuracy of suchpredictions. Once again, the human mind is not equipped to train such AImodels given the vast volume of training data and the machine, i.e.,non-human learning required to perform such operation and training.

In particular, generating one or more of the various types of datadescribed herein for a given user is performed on an automated basisbased on the various AI functions described herein within a short timeframe (such as less than a millisecond, less than 100 milliseconds, lessthan a second, less than a minute). These functions cannot feasibly beperformed by the human mind, for example based upon: the human mind notbeing able to feasibly perform one or more given functions within theshort time frame with an accuracy of output and/or consistency of outputattained by the vehicle transaction support system 100; the human mindnot being able to feasibly perform one or more given functions withinthe short time frame due to the computational complexity of performingthese functions; the human mind not being able to feasibly perform oneor more given functions within the short time frame due to processingcomplexity of processing a large required amount of input data to eachfunction, such as a large number of responses and/or large amount oftext and/or image data being processed; the human mind not being able tofeasibly perform one or more given functions within the short time framedue to searching for and/or retrieval of the appropriate input data froma large amount data, such as hundreds, thousands, and/or millions ofdifferent data elements; the human mind not being able one or more givenfunctions within the short time frame utilizing only pen and paper; thehuman mind not being able to feasibly generate one or more of thevarious data described herein within the same short time frame utilizingonly pen and paper; and/or other reasons.

Further examples including many optional functions and features of thevehicle transaction support system 100 are described in conjunction withthe Figures that follow.

FIG. 2A presents an embodiment of mobile device 120. Each mobile device120 can include one or more mobile processing devices 230 that eachinclude a processing circuit, one or more mobile memory devices 240, oneor more mobile input devices 250, one or more mobile network interfaces260 operable to more support one or more communication links via thenetwork 150 indirectly and/or directly, and/or one or more clientdisplay devices 270, connected via bus 280. While a particular busstructure is shown for purposes of illustration in a block diagram,other structures including multiple buses and/or direct connectionsbetween functional blocks. Mobile application 202 can interact with oneor more of the subsystems 102, 104, 106, 108, 110 and/or 112 of thevehicle transaction support system 100. Each mobile device 120 canreceive the application data from a corresponding subsystem 101 vianetwork 150 by utilizing network interface 260, for storage in the oneor more memory devices 240. In various embodiments, some or all mobiledevices 120 can include a computing device associated with a user of oneor more subsystems 101 as described herein.

In various embodiments, the memory device 240 can store executableinstructions included in an operating system 204, mobile application 202and/or other application, utility or routine, that, when executed by theprocessing device 230, facilitate the performance of the operations bythe mobile device 120. In particular, the one or more processing devices230 can generate an interactive interface 275 on the one or more mobiledisplay devices 270 in accordance with the mobile application 202. Theuser can provide input in response to menu data, selectable links,prompts and/or other media presented by the interactive interface viathe one or more client input devices 250. The client input devices 250can include a microphone, mouse, keyboard, touchscreen of display device270 itself or other touchscreen, and/or other device allowing the userto interact with the interactive interface 275. The one or moreprocessing devices 230 can process data and/or send raw or processeddata to the corresponding subsystem 101, and/or can receive and/orgenerate new data in response for presentation via the interactiveinterface 275 accordingly, utilizing network interface 260 tocommunicate bidirectionally with one or more subsystems 101 and/or orother systems via the network 150.

FIG. 2B presents an embodiment of one or more subsystems 101, which canbe utilized in conjunction with subsystem 102, 104, 106, 108, 110 and/or112. The subsystem(s) 101 can include, individually or collectively, oneor more subsystem processing devices 235 that each include a processingcircuit, one or more subsystem memory devices 245, and/or one or moresubsystem network interfaces 265, connected via bus 285. While aparticular bus structure is shown for purposes of illustration in ablock diagram, other structures including multiple buses and/or directconnections between functional blocks. The subsystem memory devices 245can store executable instructions that, when executed by the one or moresubsystem processing devices 235, facilitate the performance ofoperations by the subsystems 101, as described for each subsystemherein.

FIG. 3A is a schematic block diagram of a vehicle transaction supportsystem in accordance with various embodiments. In particular, a vehicletransaction support system 100 is presented that includes a subsystemprocessing device 235, subsystem network interface 265, and subsystemsmemory device 245 that support the operation of subsystem 102, 104, 106,108, 110 and/or 112 and one or more mobile devices 120. In particular,the subsystem memory device 245 of vehicle transaction support system100 stores instructions to execute a data preprocessor 302, a customergrading engine 304, a vehicle recommendation engine 306, a transactionrecommendation engine 308, a transaction adjustment engine 310, an agentsupport engine 312 and also stores one or more vehicle databases 115 toprovide the functionality, for example, of the prequalificationsubsystem 102, the customer grading subsystem 104, the vehicle selectionsubsystem 106, the transaction recommendation subsystem 108, the termsadjustment subsystem 110, the agent subsystem 112 and/or other supportsubsystems for providing automated vehicle transaction support to one ormore customers or agents via the mobile device 120. While shown as beingimplemented as a single platform with a single subsystem processingdevice 235, single subsystem network interface 265, and singlesubsystems memory 245, any combinations of subsets of the subsystems 101can be implemented via separate platforms each having their ownsubsystem processing device 235, subsystem network interface 265, andsubsystem memory 245.

The mobile device 120 executes the mobile vehicle transaction supportapplication 202 in conjunction with an operating system 204 or othergeneral purpose application, a communication application and/or aspecial purpose mobile application to bidirectionally communicate dataover network 150 via the mobile network interface 260, to provide theinteractive interface 275 for display via the mobile display device 270.When executed via the mobile processing device 230, the mobile vehicletransaction support application 202 interacts with the vehicletransaction support system 100 to provide the various functions andfeatures described herein. In particular, the mobile vehicle transactionsupport application 202 and the vehicle transaction support system 100cooperate, permitting user(s) of mobile device(s) 120, such as customersand agents to utilize the vehicle transaction support system 100. Itshould be noted that this cooperation can include functions solelyperformed by the mobile device 120, functions performed solely by thevehicle transaction support system 100 and functions performed jointlyvia both devices. Furthermore, some or all of the functions attributedto the vehicle transaction support system 100 can be incorporated in themobile device 120 and/or performed by the mobile vehicle transactionsupport application 202, operating system 204 or other application,utility or routine.

Several functions performed by the vehicle transaction support system100 can be described in conjunction with FIG. 3B which presents a flowdiagram in accordance with various embodiments. These functions include:

-   -   Receiving, via the data preprocessor 302, customer input data        generated via customer interaction with a graphical user        interface of a mobile device and generating customer        prequalification data and other customer profile data from the        customer input data;    -   Generating, based on the customer prequalification data and/or        extended bureau data and utilizing a customer grading engine 304        having an artificial intelligence model trained via machine        learning, a customer grade that predicts a likelihood of        transaction default associated with a customer;    -   Generating, based on customer profile data that includes the        customer grade and the other customer profile data and utilizing        a vehicle recommendation engine 306 having an artificial        intelligence model trained via machine learning, vehicle options        data that indicate particular vehicles selected from the vehicle        database 115; and    -   Generating, based on at least a portion of the customer profile        data (that may include the customer grade) and the vehicle        options data and utilizing a transaction recommendation engine        308 having a third artificial intelligence model trained via        machine learning, terms data associated with each of the        vehicles selected from the vehicle database.

For example, the customer prequalification data includes, if available,logical, text and/or numerical values associated with any of thecriteria indicated below in Table 1 below:

TABLE 1 Lived in US (years), FICO or other commercial credit score,Residence Type, Length of Residence (months), Length of Employment,Length of Previous Employment, Current Insurance, CurrentTransportation, Prior Credit, Unsatisfied Judgments (amount),Repossession (years ago), Application Month,

In various embodiments, the extended bureau data includes, for example,customer data received from a credit bureau that is different andseparate from a traditional credit score, such as a FICO® credit score,VantageScore , Lexis Nexis® score, Experian® score, Equifax® score,Transunion® score or other commercially credit score, but insteadincludes data such as credit bureau or other bureau data indicating morespecific customer metrics such as customer identifying data, creditaccount data, credit inquiry data, public records data, etc. Examples ofthe extended bureau include logical, text and/or numerical valuesassociated with any of the criteria indicated below in Table 2 below:

TABLE 2 Age Oldest Record, Phone Age Oldest Record, Current Address AgeOldest Record, Lien Filed Total, Previous Address Last Sales Price,Phone Age Newest Record, Address Recent Economic Trajectory,Professional License Type, Subject Phone Recent Count, Phone Other AgeNewest Record, Address Recent Economic Trajectory 2, Input AddressAffordable Market Value, SSN Issue State, Previous Address Age LastSale,

In various embodiments, the other customer profile data includes, forexample, any or all of the customer prequalification data and also, ifavailable, logical, text and/or numerical values associated with any ofthe criteria indicated below in Table 3 below:

TABLE 3 Household Size, Dependents Under 18, Driver's License Years,Financed Vehicles Before, Current Transportation, Length of Residence(months), Lived in US (years), Savings, Full Time Jobs, FICO score,Length of Employment, Children, Application Month, Age, Marital Status,Length of Previous Employment, Part Time Jobs Cell Phone Type, Other IDType, Ups Count, Checking Account, Employment Status, Current DebtBalance, No. of Phones, Adults living in Residence, Down Payment ComingFrom, Residence Type, Monthly Miles, Current Insurance, Type of Work,State, Income Pay Mode, Passport Country, Born in US, Dependents over18, Residence zip code

Furthermore, the customer profile data can include any or all of theother customer profile data and/or the customer grade and furtherinformation included in customer input data and/or additional extendedbureau data. It should be noted that Tables 1, 2 and 3 present merelyexamples of data fields and that other types of data can be used, inaddition or in the alternative to the data fields shown.

In various embodiments, the operations of the vehicle transactionsupport system 100 can further include:

-   -   sending the vehicle options data and terms data for display via        the graphical user interface of a mobile device;    -   obtaining updated terms data generated via customer interaction        with the graphical user interface;    -   comparing the updated terms data to term limits data;    -   generating transaction feedback data indicating acceptance of        the updated terms for display via the graphical user interface,        when the updated terms data compare favorably to the term limits        data; and generating transaction feedback data indicating        rejection of the updated terms for display via the graphical        user interface, when the updated terms data compare unfavorably        to the term limits data.

FIG. 3C is a graphic diagram of predicted cumulative likelihood oftransaction default in accordance with various embodiments. Aspreviously discussed, the customer grading engine 306 generates acustomer grade that predicts a likelihood of transaction defaultassociated with a customer. In the example shown, the customer gradedetermined as one of a plurality of discrete values [A+, A, B, C, D, E ]based on a predicted cumulative likelihood of transaction default over apredetermined term, such as 6, 12, 24 months or some over value. Withthis metric, A+ is the most desirable grade and E is the least desirablegrade.

In operation, the cumulative likelihood of transaction default ispredicted by the AI model of the customer grading engine 306 as either adiscrete value corresponding to one of the plurality of discrete valuesor as an numerical score that is fit to the closest one of the pluralityof discrete values to generate the customer grade. While a particularcustomer grading metric is shown, customer grades corresponding to othermetrics proportional to, corresponding to, indicating and/or otherwiseassociated with the likelihood of transaction default could likewise beemployed.

FIG. 3D is a schematic block diagram of a customer grading engine inaccordance with various embodiments. In the example shown, the AI modelof the customer grading engine 304 is an ensemble model that includes nauto sub-models configured to process the customer prequalification dataand the extended bureau data and furthermore a master sub-modelconfigured to generate the customer grade by processing the customerprequalification data and the extended bureau data together with outputsof the n sub-models.

An autoencoder is a type of artificial neural network used to learnefficient data codings in an unsupervised manner. The aim of anautoencoder is to learn a representation (encoding) for a set of data,by training the network to ignore signal “noise”. Along with thereduction side, a reconstructing side is learnt, where the autoencodertries to generate from the reduced encoding a representation as close aspossible to its original input, hence its name. By learning to replicatethe most salient features in the training data set under some of theconstraints described previously, the model is encouraged to learn howto precisely reproduce the most frequent characteristics of theobservations. When facing anomalies, the model should worsen itsreconstruction performance. In most cases, only data with normalinstances are used to train the autoencoder; in others, the frequency ofanomalies is so small compared to the whole population of observations,that its contribution to the representation learnt by the model could beignored. After training, autoencoder operates to reconstruct normal datavery well, while failing to do so with anomaly data which theautoencoder has not encountered. Reconstruction error of a data point,which is the error between the original data point and its lowdimensional reconstruction, is used as an anomaly score to detectanomalies.

In various embodiments, the sub-models are implemented via autoencoderstrained to distinguish input data, e.g., customer prequalification dataand extended bureau data, indicating no default from detected anomaliesindicating potential default. The following steps can be utilized totrain these autoencoders:

-   -   Reference data, e.g., historical customer prequalification data        and extended bureau data, is split into a training data and a        validation data set. Only accounts without a default are        selected to train the auto encoders. This is done to enable the        network to learn the salient features of the good accounts and        produce a large abnormality signal when scoring bad accounts.    -   A large set of neural network setups (10, 25, 50, 100 or more)        are tested, along with associated hyper parameters of training        steps.    -   The top n neural networks are selected as the n sub-models based        on the performance of the models on the training and validation        dataset. For example, n can be equal to an integer value such as        2, 3, 4, 5, 6, 7, 8 . . . , however greater values can be used        in more complex implementations.

In various embodiments, the master sub-model 320 is implemented via anoptimized distributed gradient boosting AI model, such as an XGBoostlibrary designed to be highly efficient, flexible and portable. Forexample, the master sub-model 320 can provide a parallel tree boostingthat solves for the customer grade problems in a fast and accurate way.

The master sub-model 320 can be trained as follows:

-   -   The input data, e.g., customer prequalification data and        extended bureau data, is passed through n sub-models described        above and their respective outputs are appended to the reference        data.    -   Reference data e.g., historical customer prequalification data        and extended bureau data, is split into training data and        validation data.    -   A large set of hyper parameters for the master sub-model 320 are        tested, such as:        -   a. Learning rate        -   b. Subsample        -   c. Colsample_bytree        -   d. Number of estimators        -   e. Max depth        -   f. Gamma        -   g. Min. child weight        -   h. Etc.            Performance of each candidate master sub-model 320 is            evaluated on training and validation samples. The model            which shows the most stability across training and            validation with high Kolmogorov-Smirnov (KS) and receiver            operating characteristic (ROC) metrics is selected.

FIG. 3E is a schematic block diagram of a customer recommendation enginein accordance with various embodiments. In the example shown, the AImodel of the customer recommendation engine 306 is an ensemble modelthat includes: a vehicle category selection sub-model 330 a vehicleparameter selection sub-model 332 and a vehicle search engine 334. Thevehicle database(s) 115 store vehicle data associated a number ofvehicles of various categories, ages, mileage, prices or price rangesetc., that are available for sale.

The vehicle category selection sub-model 330 is configured to generateone or more selected vehicle categories from of a plurality of vehiclecategories based on the customer profile data including, for example,the customer grade. It should be noted that the customer grade could begenerated by the customer grading engine 304 or some default parametersuch as an C or E grade if prequalification has not occurred. Thefunction of the vehicle category selection sub-model 330 is to generatea prediction the category of vehicle user may wish to buy.

In various embodiments, the vehicle category selection sub-model 330 isimplemented via an optimized distributed gradient boosting AI model,such as an XGBoost library designed to be highly efficient, flexible andportable. The vehicle category selection sub-model 330 can be trained asfollows:

-   -   Reference data, e.g., historical customer profile data along        with actual vehicle category selections, is split into training        data and validation data.    -   A large set of hyper parameters for the vehicle parameter        selection sub-model 332 are tested, such as:        -   i. Learning rate        -   j. Subsample        -   k. Colsample_bytree        -   l. Number of estimators        -   m. Max depth        -   n. Gamma        -   o. Min. child weight        -   p. Etc.            Performance of each candidate vehicle category selection            sub-model 330 is evaluated based on training and validation            samples representing historical customer category            selections. The model which shows the most stability across            training and validation with high Kolmogorov-Smirnov (KS)            and receiver operating characteristic (ROC) metrics is            selected.

The vehicle parameter selection sub-model 332 is configured to generatea plurality of vehicle parameters for the selected vehicle categorybased on the customer profile data and the customer grade. For example,the vehicle parameters can include a vehicle category such as largetruck, small truck, sport utility vehicle (SUV), vehicle price or pricerange, vehicle mileage, vehicle age, etc. In various embodiments, thevehicle parameter selection sub-model 332 is implemented via aninformation filtering system such as a collaborative filter that seeksto predict the “rating” or “preference” a user would give to a categoryof vehicle. In various embodiments, the vehicle parameter selectionsub-model 332 identifies similar vehicles selected by similar usersbased on training data that includes historical sales records andgenerates vehicle parameters corresponding to these similar vehicles.

The following steps can be used to train the vehicle parameter selectionsub-model 332:

-   -   Reference data, e.g., historical customer profile data and/or        customer grade along with along with actual vehicle category        selections, is scored through the vehicle category selection        sub-model 330 described above and the top m categories are        estimated for each record, where m=3, 4, 5 or higher.    -   Reference data, e.g. vehicles selected by past customers along        with their customer profile data and/or customer grade, is split        into training data and validation.    -   A collaborative filter is trained using the customer profile        data of the customers, historical purchase decisions and        predicted categories.    -   Performance of the model is evaluated on training and validation        samples. The candidate model which shows most stability across        the training and validation is selected.

The vehicle search engine 334 is configured to generate vehicle optionsdata indicating a set of selected vehicles based on a search the vehicledatabase(s) 115 for vehicles having corresponding vehicle data thatcompares favorably to the vehicle parameters. Database search techniquescan be used to select p, a predetermine number, of vehicles—that havethe greatest match to the vehicle parameters. Otherwise, database searchtechniques can be used to select all vehicles that match the vehicleparameters within a predetermined tolerance.

FIG. 3F is a schematic block diagram of a transaction recommendationengine in accordance with various embodiments. In the example shown, theAI model of the transaction recommendation engine 308 is an ensemblemodel that includes a transaction parameter selection sub-model 342 anda terms selection sub-model 344. The transaction parameter selectionsub-model 342 is configured to generate, based on the customer profiledata, transaction parameters that indicate a collection/pool of dealterms predicted for acceptance—sets of deal terms each characterized by,for example, down payments, minimum down payments, weekly, biweekly ormonthly payment amounts, interest rates, the number of term monthsand/or the maximum number of term months, predicted for the customer. Invarious embodiments, the transaction parameter selection sub-model 342is implemented via an information filtering system such as acollaborative filter that seeks to predict the “rating” or “preference”a user would give to a particular set of deal terms. In variousembodiments, the transaction parameter selection sub-model 342identifies a collection/set of transaction parameters (e.g., a pool ofdeal terms) based on a collaborative filtering based on historical salesrecords for similar customers filtered for non-defaulting accounts. Thisis done to improve the risk profile of the recommended deals terms thathave a high likelihood to book.

The terms selection sub-model 344 is configured to generate, based onthe transaction parameters and the vehicle options data, the terms dataassociated with each of the vehicles selected from the vehicle database115. For example, the terms selection sub-model 344 scores the dealterms indicated by the transaction parameters using an NPV model forcash flow estimation. The deals are then ranked ordered and top q, apredetermined number of deals or including both selected vehicles andcorresponding deal terms, to be presented to the customer. In thealternative, all deals having scores that exceed a score threshold canbe presented to the customer.

An example implementation of the terms selection model 344 is presentedin conjunction with FIG. 3G that follows. In the example shown, termsselection model 344 is itself an ensemble AI model that includes: netpresent value (NPV) sub-models 352 and 354, a probability of default(PD) sub-model 356 and an NPV function and terms generation model 350.

In various embodiments, the NPV sub-model 352 is trained to generate,based on the customer profile, the transaction parameters, and thevehicle options data, a net present value based on historical datarepresenting non-defaulting transactions. The NPV sub-model 346 istrained to generate, based on the customer profile data, the transactionparameters, and the vehicle options data, a net present value based onhistorical data representing defaulting transactions. The PD sub-model356 is trained to generate, based on the customer profile data, thetransaction parameters, and the vehicle options data, the probability ofdefault. The net present value function and terms generation sub-model350 is configured to generate the terms data associated with each of thevehicles selected from the vehicle database, based on outputs from theNPV sub-model 352, the NPV sub-model 354 and the NPV sub-model 356. Forexample, the net present value (NPV) sub-models 352 and 354 predict twoseparate NPVs, one trained based on non-defaulted training data and theother trained based on defaulted training data. The NPV sub-models 352and 354 can be implemented, for example, via Kernel Ridge Regression,XGBoost regression, Lasso, Elastic Net, and/or other AI model that isnormalized based on deal NPV ratios calculated using the followingformula:

NPV_ratio=deal_npv/(Sales Price−Total Down Payment)

where the sales price is gathered for a selected vehicle indicated bythe vehicle options data and total down payment indicated by thetransaction parameters. These two NPV sub-models 352 and 354 generaterespectively two values, NPV_non-defaulted and NPV_defaulted, based oncustomer profile data, the pool of deal terms indicated by thetransaction parameters and the vehicle options data representing thevehicle selected from the vehicle database(s) 115.

The PD sub-model 356, for example, can be implemented similarly to thecustomer grading engine 304, but also trained on a training data thatindicates deal terms and further including historical data both with andwithout a traditional credit score. In operation, the PD sub-model 356uses the pool of deal terms indicated by the transaction parameters, inaddition to the customer profile data to generate a PD value that isindicative of the likelihood of transaction default.

The NPV function and terms generation sub-model 350 generates, for eachvehicle selection indicated by the transaction terms, a final NPV valuethat can, for example, be calculated as follows:

Final_NPV=(NPV_non-defaulted*(1−PD_value))+(NPV_defaulted*PD_value)

The deals can then be ranked ordered based on the top r final NPVs orthe group of final NPVs that are above an acceptable final NPVthreshold. In addition or in the alternative, the required down paymentcan be calculated as, for example:

Down_Payment=Sales Price−Final_NPV

The terms data indicates, for each selected vehicle, the correspondingfinal deal terms generated in the fashion. For example, this terms datacan indicate, for each selected vehicle indicated by the vehicle optionsdata, a minimum down payment such as Down_Payment, a recommended downpayment, a maximum number of term months, a recommended number of termmonths, an annual percentage rate (APR) or other indicator of interestrate and/or a weekly, biweekly or monthly payment amount.

FIG. 3H is a schematic block diagram of a vehicle transaction supportsystem in accordance with various embodiments. As previously discussed,the operations of the vehicle transaction support system 100 can furtherinclude:

-   -   obtaining vehicle selection data and updated terms data        generated via customer interaction with a graphical user        interface (GUI), such as interactive interface 275 of mobile        device 120;    -   comparing the updated terms data to term limits data;    -   generating transaction feedback data indicating acceptance of        the updated terms for display via the graphical user interface,        when the updated terms data compare favorably to the term limits        data; and    -   generating transaction feedback data indicating rejection of the        updated terms for display via the graphical user interface, when        the updated terms data compare unfavorably to the term limits        data.

For example, the vehicle options data with terms data sent to the mobiledevice 120 includes, for each of the vehicles selected by the vehicletransaction support system 100 from the vehicle database, a recommendeddown payment, a recommended number of weekly, biweekly or monthlypayments, an annual percentage rate (APR) or other indicator of interestrate and a weekly, biweekly or monthly payment amount. Customerinteraction with the GUI of mobile device 120 generates vehicleselection data and updated terms data that is sent to the vehicletransaction support system 100. For example, the vehicle selection datacan indicate a selection, by the customer via the interaction with theGUI, a particular one of the vehicles indicated by the vehicle optionsdata. The updated terms data can include a selected modification to therecommended down payment and/or the recommended number of weekly,biweekly or monthly payments. The transaction adjustment engine 310operates to compare the updated terms data to term limits data, such asmaximum number of monthly payments and/or the minimum down payment.If/when the updated terms data compare favorably to the term limitsdata, i.e. the down payment, if modified, is greater than or equal tothe minimum down payment and the number of weekly, biweekly or monthlypayments, if modified, is less than or equal to the maximum number ofweekly, biweekly or monthly payments, transaction feedback dataindicating acceptance of the updated terms and an updated weekly,biweekly or monthly payment is generated and sent for display via thegraphical user interface of mobile device 120. If/when the updated termsdata compare unfavorably to the term limits data, i.e. the down payment,if modified, is less than the minimum down payment or the number weekly,biweekly or monthly payments, if modified, is greater than the maximumnumber of weekly, biweekly or monthly payments, transaction feedbackdata indicating rejection of the updated terms is generated and sent fordisplay via the graphical user interface of mobile device 120.

As previously discussed, some or all of the functions attributed to thevehicle transaction support system 100 can be incorporated in the mobiledevice 120 and/or performed by the mobile vehicle transaction supportapplication 202. In various embodiments, the terms adjustment functionsassociated with the terms adjustment subsystem 110 and the transactionadjustment engine 310 can be implemented instead via the mobile vehicletransaction support application 202 and/or other applications, utilitiesor routines of the mobile device 120, based on vehicle options and termsdata from the vehicle transaction support system 100, etc.

FIG. 3I is a schematic block diagram of a transaction adjustment enginein accordance with various embodiments. In the example shown, thetransaction adjustment engine 312 or the mobile vehicle transactionsupport application 202 includes a comparison function 360 such as acomparison routine or other comparator that compares the updated termsdata to term limits data, such as maximum number of monthly paymentsand/or the minimum down payment.

If/when the updated terms data compare favorably to the term limitsdata, i.e. the down payment, if modified, is greater than or equal tothe minimum down payment and the number weekly, biweekly or monthlypayments, if modified, is less than or equal to the maximum number ofweekly, biweekly or monthly payments, transaction feedback dataindicating acceptance of the updated terms and an updated weekly,biweekly or monthly payment is generated and sent for display via thegraphical user interface of mobile device 120. If/when the updated termsdata compare unfavorably to the term limits data, i.e. the down payment,if modified, is less than the minimum down payment or the number weekly,biweekly or monthly payments, if modified, is greater than the maximumnumber of weekly, biweekly or monthly payments, transaction feedbackdata indicating rejection of the updated terms is generated and sent fordisplay via the graphical user interface of mobile device 120.

FIG. 3J is a schematic block diagram of a vehicle transaction supportsystem in accordance with various embodiments. In various embodiments,the operation of the mobile device 120 can be controlled by an agent toselectively place the mobile device 120 in an agent mode where some orall of the functions of the vehicle transaction support system 100 areaccessible via the mobile device 120 or a customer mode where only aselected subset of the functions of the vehicle transaction supportsystem 100 are accessible via the mobile device 120. The selected subsetof the functions available in customer mode can include, for example,customer functions requiring customer interaction with the interactiveinterface 275—but excluding agent-only functions of the mobile device.For example, the operations of the vehicle transaction support system100 can include:

-   -   collecting customer input data in either the agent or customer        mode of operation of the mobile device;    -   sending the vehicle options data and terms data for display via        the graphical user interface of a mobile device in either the        agent or customer mode of operation of the mobile device;    -   obtaining vehicle selection data and updated terms data        generated via customer interaction with the graphical user        interface of mobile device 120 in either the agent or customer        mode of operation of the mobile device;    -   sending transaction feedback data for display via the graphical        user interface of mobile device 120 in either the agent or        customer mode of operation of the mobile device;    -   communicating agent data with the mobile device 120 in an agent        mode of the mobile device, wherein the agent data is neither        displayed nor displayable via the graphical user interface of        the mobile device in the customer mode of operation.        Furthermore, switching from customer mode to agent mode and/or        from agent mode to customer mode can require agent        authentication via password, biometric authentication,        two-factor authentication of other security or authentication        procedure.

FIGS. 4A-4E are screen displays of an interactive interface inaccordance with various embodiments. As previously discussed, customerinput data is generated via interaction with a graphical user interfaceof a mobile device. In particular, screen displays of a graphical userinterface, such as interactive interface 275 of mobile device 120 arepresented that indicate the collection of portions of the customer inputdata for a company, Tricolor. The customer input data collected in thisfashion is used to used to generate customer prequalification data andother customer profile data, by for example, storing the various fieldsof input data in corresponding fields of the generate customerprequalification data and/or the other customer profile data, asappropriate.

Turning now to FIG. 4A, a screen display is shown where a user such as acustomer or agent is entering and/or viewing personal information suchas name, social security number or tax ID, date of birth, form ofidentification and ID number, ID expiration date, driver licenseinformation on family members, address, etc. Other customer input dataindicates the stage of the customer in the process, e.g. application ortest drive, a vehicle of interest, the availability of a trade-in, etc.

In FIG. 4B, a screen display is shown where a user such as a customer oragent is entering and/or viewing employment information such asemployment status, length of current employment, employer, job category,position/titles, company phone and address, net income, work shift,payment method, deposit information, tax information, sources ofadditional income, etc. In FIG. 4C, a screen display is shown where auser such as a customer or agent is entering and/or viewing financialinformation such as prior vehicle financing, type and source of downpayment, prior company credit, prior company employment, use of thevehicle for ride-sharing, money sent to others, personal loans, checkcashing services, payment methods, bank/credit account information,current loan information, current loan payment status, auto insuranceinformation, major accidents, etc.

In FIG. 4D, a screen display is shown where a user such as a customer oragent is entering and/or viewing other information such as maritalstatus, number of dependents, number of people in the household, numberof drivers, household members employed, primary use of the vehicle,monthly mileage, method of transportation to work, time having adriver's license, type of cellphone plan and carrier, time with currenttelephone number, country of birth, time in the U.S.,citizenship/residency/immigration status, type of education, etc. InFIG. 4E, a screen display is shown where a user such as an agent canview information regarding the status of verifications, uploadedsupporting documents, etc.

FIGS. 5A-5F are screen displays of an interactive interface inaccordance with various embodiments. In particular, screen displays of agraphical user interface, such as interactive interface 275 of mobiledevice 120 are presented. As previously discussed, the vehicletransaction support system 100 can generate vehicle options data withterms data associated with vehicles selected from a vehicle database115. This information can be sent to mobile device 120 for display.Vehicle selection data and updated terms data can be generated viacustomer interaction with the graphical user interface of mobile device120 and transaction feedback data can be displayed via the graphicaluser interface of mobile device 120.

Turning now to FIG. 5A, a screen display is presented that includes avehicle bar with vehicle options data including pictures and summaryinformation pertaining to a plurality of recommended vehicles selectedfrom the vehicle database 115. A right arrow presented in the rightmostportion of the vehicle bar allows the user to scroll to otherrecommended vehicles. The leftmost vehicle, a 2019 Toyota Corolla with41,474 miles has been highlighted and further vehicle options data withterms data for this vehicle are displayed including sales price, minimumdown payment, maximum number of monthly payments, a vehicle stock numberand a recommended down payment. A right arrow next to an enlargedvehicle picture allows the user to scroll through other pictures of thehighlighted vehicle. Tabs above the enlarged vehicle picture allow theuser to view further vehicle details and lead details regarding thecustomer.

In FIG. 5B, a lower portion of the screen display is shown, where theuser has chosen a 2014 Ford F-150 to review. The vehicle bar has beendisabled, enabling a vehicle image bar below the enlarged vehiclepicture that shows the user's navigation status through a plurality ofvehicle images. In this case the user has selected updated terms thatinclude a down payment of 2590 and a 49 month loan that yields abiweekly payment of $393 at 21.4% interest. Feedback data is presentedthat highlights the minimum down payment requirement of $2850 and thatfurther indicates that more down payment is needed. In FIG. 5C, the userhas opted to review the vehicle details via selection of the vehicledetails tab.

In FIG. 5D, the user has changed to reviewing a different recommendedvehicle, a 2019 Dodge Ram 1500. The same down payment of $2590 and 49month loan length have been selected. In this case, the an additionalpop-up message has been generated indicating more down payment isrequired. In FIG. 5E, the user has generated updated terms dataincreasing the down payment to $3600, and amount above the minimum. Thebiweekly payment has decreased to $389 and the Save to CDE button hasbeen enabled in response to feedback data allowing these deal terms andvehicle to be selected for processing. In FIG. 5F, the user hasgenerated updated terms data with a 51 month loan, changing the biweeklypayment to $377. Because the down payment is greater than or equal tothe minimum and the number of months is still less than or equal to themaximum, feedback data causes the Save to CDE button to be enabledallowing these deal terms and vehicle to be selected for processing.

FIGS. 6A-6B are portions of screen displays of an interactive interfacein accordance with various embodiments. As previously discussed, theoperation of the mobile device 120 can be controlled by an agent toselectively place the mobile device 120 in an agent mode where some orall of the functions of the vehicle transaction support system 100 areaccessible via the mobile device 120 or a customer mode where only aselected subset of the functions of the vehicle transaction supportsystem 100 are accessible via the mobile device 120. In FIG. 6A, ascreen display is shown where the mobile device 120 is in the agent modeof operation and agent data is presented for display. In this case,information on two different customers is presented. The first customerhas been prequalified as indicated by the green “?” icon. The customer'snumber and numerical identifier are shown in addition to the time andlot location visited, the salesperson and a selected vehicle ofinterest. The second has not been prequalified as indicated by theorange “?” icon and there has been vehicle selected. In FIG. 6B, theagent can selected a customer and indicate if a selected customer iscurrently on the lot.

FIGS. 7A-7D are flowchart representations of methods in accordance withvarious embodiments. In particular, methods are shown for use inconjunction with one or more functions and features described herein.Turning now to FIG. 7A, step 1000 includes generating, based on customerprequalification data and extended bureau data and utilizing a customergrading engine having a first artificial intelligence model trained viamachine learning, a customer grade that predicts a likelihood oftransaction default associated with a customer. Step 1002 includesgenerating, based on customer profile data that includes the customergrade and other customer profile data and utilizing a vehiclerecommendation engine having a second artificial intelligence modeltrained via machine learning, vehicle options data that indicatesvehicles selected from a vehicle database. Step 1004 includesgenerating, based on at least a portion of the customer profile data andthe vehicle options data and utilizing a transaction recommendationengine having a third artificial intelligence model trained via machinelearning, terms data associated with each of the vehicles selected fromthe vehicle database.

In various embodiments, the extended bureau data includes customer datareceived from a credit bureau that does not include a credit score. Themethod can further include: receiving customer input data generated viacustomer interaction with a graphical user interface; and generating thecustomer prequalification data and the other customer profile data fromthe customer input data. The method can further include sending thevehicle options data and terms data for display via a graphical userinterface of a mobile device. The method can further include: obtainingvehicle selection data and updated terms data generated via customerinteraction with the graphical user interface; comparing the updatedterms data to term limits data; generating transaction feedback dataindicating acceptance of the updated terms data for display via thegraphical user interface, when the updated terms data compare favorablyto the term limits data; and generating transaction feedback dataindicating rejection of the updated terms for display via the graphicaluser interface, when the updated terms data compare unfavorably to theterm limits data. The method can further include: sending the vehicleoptions data and terms data for display via a graphical user interfaceof a mobile device in a customer mode of operation of the mobile device;and communicating agent data with the mobile device in an agent mode ofthe mobile device, wherein the agent data is not displayed via thegraphical user interface of the mobile device in the customer mode ofoperation.

In various embodiments, the first artificial intelligence model is anensemble model that includes: a plurality of auto encoding sub-modelsconfigured to process the customer prequalification data and theextended bureau data; and a master sub-model configured to generate thecustomer grade by processing the customer prequalification data and theextended bureau data together with outputs of the plurality of autoencoding sub-models. The second artificial intelligence model can be anensemble model that includes: a vehicle category selection sub-modelconfigured to generate a selected vehicle category from a plurality ofvehicle categories based on the customer profile data; a vehicleparameter selection sub-model configured to generate a plurality ofvehicle parameters for the selected vehicle category based on thecustomer profile data; and a vehicle search engine configured togenerate the vehicles selected from the vehicle database based on asearch of the vehicle database for vehicles having vehicle data thatcompares favorably to the vehicle parameters.

In various embodiments, the third artificial intelligence model is anensemble model that includes: a transaction parameter selectionsub-model configured to generate, based on the at least a portion of thecustomer profile data, transaction parameters that indicate a collectionof deal terms predicted for acceptance; and a terms selection sub-modelconfigured to generate, based on the transaction parameters and thevehicle options data the terms data associated with each of the vehiclesselected from the vehicle database. The terms selection sub-model canitself be an ensemble model that includes: a first sub-model trained togenerate, based on the at least a portion of the customer profile data,the transaction parameters, and the vehicle options data, a net presentvalue based on non-defaulting transactions; a second sub-model trainedto generate, based on the at least a portion of the customer profiledata, the transaction parameters, and the vehicle options data, a netpresent value based on defaulting transactions; a third sub-modeltrained to generate, based on the at least a portion of the customerprofile data, the transaction parameters, and the vehicle options data,probability of default; and a net present value function configured togenerate the terms data associated with each of the vehicles selectedfrom the vehicle database, based on outputs from the first sub-model,the second sub-model and the third sub-model.

Turning now to FIG. 7B, step 1010 includes receiving customer input datagenerated via customer interaction with a graphical user interface. Step1012 includes generating customer prequalification data from thecustomer input data. Step 1014 includes receiving extended bureau datafrom a bureau database. Step 1016 includes generating, based on customerprequalification data and extended bureau data and utilizing a customergrading engine having an artificial intelligence model trained viamachine learning, a customer grade that predicts a likelihood oftransaction default associated with a customer. Step 1018 includessending the vehicle options data and terms data for display via agraphical user interface of a mobile device.

Turning now to FIG. 7C, step 1020 includes receiving customer input datagenerated via customer interaction with a graphical user interface. Step1022 includes generating customer profile data from the customer inputdata. Step 1024 includes receiving extended bureau data from a bureaudatabase. Step 1026 includes generating, based on customer profile dataand extended bureau data and utilizing a vehicle recommendation enginehaving an artificial intelligence model trained via machine learning,vehicle options data that indicates vehicles selected from a vehicledatabase. Step 1022 includes sending the vehicle options data fordisplay via a graphical user interface of a mobile device.

Turning now to FIG. 7D, step 1030 includes receiving customer input datagenerated via customer interaction with a graphical user interface. Step1032 includes generating customer profile data from the customer inputdata. Step 1034 includes obtaining vehicle options data associated withvehicles selected from a vehicle database. Step 1036 includesgenerating, based on the customer profile data and the vehicle optionsdata and utilizing a transaction recommendation engine having anartificial intelligence model trained via machine learning, terms dataassociated with each of the vehicles selected from the vehicle database.Step 1038 includes sending the vehicle options data with the terms datafor display via a graphical user interface of a mobile device.

While the foregoing has focused on systems and methods for supportingvehicle transactions, these systems and methods could likewise be usedsimilarly for other assets including homes, boats, investments, home andapartment rentals, and other assets and transactions.

It is noted that terminologies as may be used herein such as bit stream,stream, signal sequence, etc. (or their equivalents) have been usedinterchangeably to describe digital information whose contentcorresponds to any of a number of desired types (e.g., data, video,speech, text, graphics, audio, etc. any of which may generally bereferred to as ‘data’).

As may be used herein, the terms “substantially” and “approximately”provides an industry-accepted tolerance for its corresponding termand/or relativity between items. For some industries, anindustry-accepted tolerance is less than one percent and, for otherindustries, the industry-accepted tolerance is 10 percent or more. Otherexamples of industry-accepted tolerance range from less than one percentto fifty percent. Industry-accepted tolerances correspond to, but arenot limited to, component values, integrated circuit process variations,temperature variations, rise and fall times, thermal noise, dimensions,signaling errors, dropped packets, temperatures, pressures, materialcompositions, and/or performance metrics. Within an industry, tolerancevariances of accepted tolerances may be more or less than a percentagelevel (e.g., dimension tolerance of less than +/−1%). Some relativitybetween items may range from a difference of less than a percentagelevel to a few percent. Other relativity between items may range from adifference of a few percent to magnitude of differences.

As may also be used herein, the term(s) “configured to”, “operablycoupled to”, “coupled to”, and/or “coupling” includes direct couplingbetween items and/or indirect coupling between items via an interveningitem (e.g., an item includes, but is not limited to, a component, anelement, a circuit, and/or a module) where, for an example of indirectcoupling, the intervening item does not modify the information of asignal but may adjust its current level, voltage level, and/or powerlevel. As may further be used herein, inferred coupling (i.e., where oneelement is coupled to another element by inference) includes direct andindirect coupling between two items in the same manner as “coupled to”.

As may even further be used herein, the term “configured to”, “operableto”, “coupled to”, or “operably coupled to” indicates that an itemincludes one or more of power connections, input(s), output(s), etc., toperform, when activated, one or more its corresponding functions and mayfurther include inferred coupling to one or more other items. As maystill further be used herein, the term “associated with”, includesdirect and/or indirect coupling of separate items and/or one item beingembedded within another item.

As may be used herein, the term “compares favorably”, indicates that acomparison between two or more items, signals, etc., provides a desiredrelationship. For example, when the desired relationship is that signal1 has a greater magnitude than signal 2, a favorable comparison may beachieved when the magnitude of signal 1 is greater than that of signal 2or when the magnitude of signal 2 is less than that of signal 1. As maybe used herein, the term “compares unfavorably”, indicates that acomparison between two or more items, signals, etc., fails to providethe desired relationship.

As may be used herein, one or more claims may include, in a specificform of this generic form, the phrase “at least one of a, b, and c” orof this generic form “at least one of a, b, or c”, with more or lesselements than “a”, “b”, and “c”. In either phrasing, the phrases are tobe interpreted identically. In particular, “at least one of a, b, and c”is equivalent to “at least one of a, b, or c” and shall mean a, b,and/or c. As an example, it means: “a” only, “b” only, “c” only, “a” and“b”, “a” and “c”, “b” and “c”, and/or “a”, “b”, and “c”.

As may also be used herein, the terms “processing module”, “processingcircuit”, “processor”, “processing circuitry”, and/or “processing unit”may be a single processing device or a plurality of processing devices.Such a processing device may be a microprocessor, micro-controller,digital signal processor, microcomputer, central processing unit, fieldprogrammable gate array, programmable logic device, state machine, logiccircuitry, analog circuitry, digital circuitry, and/or any device thatmanipulates signals (analog and/or digital) based on hard coding of thecircuitry and/or operational instructions. The processing module,module, processing circuit, processing circuitry, and/or processing unitmay be, or further include, memory and/or an integrated memory element,which may be a single memory device, a plurality of memory devices,and/or embedded circuitry of another processing module, module,processing circuit, processing circuitry, and/or processing unit. Such amemory device may be a read-only memory, random access memory, volatilememory, non-volatile memory, static memory, dynamic memory, flashmemory, cache memory, and/or any device that stores digital information.Note that if the processing module, module, processing circuit,processing circuitry, and/or processing unit includes more than oneprocessing device, the processing devices may be centrally located(e.g., directly coupled together via a wired and/or wireless busstructure) or may be distributedly located (e.g., cloud computing viaindirect coupling via a local area network and/or a wide area network).Further note that if the processing module, module, processing circuit,processing circuitry and/or processing unit implements one or more ofits functions via a state machine, analog circuitry, digital circuitry,and/or logic circuitry, the memory and/or memory element storing thecorresponding operational instructions may be embedded within, orexternal to, the circuitry comprising the state machine, analogcircuitry, digital circuitry, and/or logic circuitry. Still further notethat, the memory element may store, and the processing module, module,processing circuit, processing circuitry and/or processing unitexecutes, hard coded and/or operational instructions corresponding to atleast some of the steps and/or functions illustrated in one or more ofthe Figures. Such a memory device or memory element can be included inan article of manufacture.

One or more embodiments have been described above with the aid of methodsteps illustrating the performance of specified functions andrelationships thereof. The boundaries and sequence of these functionalbuilding blocks and method steps have been arbitrarily defined hereinfor convenience of description. Alternate boundaries and sequences canbe defined so long as the specified functions and relationships areappropriately performed. Any such alternate boundaries or sequences arethus within the scope and spirit of the claims. Further, the boundariesof these functional building blocks have been arbitrarily defined forconvenience of description. Alternate boundaries could be defined aslong as the certain significant functions are appropriately performed.Similarly, flow diagram blocks may also have been arbitrarily definedherein to illustrate certain significant functionality.

To the extent used, the flow diagram block boundaries and sequence couldhave been defined otherwise and still perform the certain significantfunctionality. Such alternate definitions of both functional buildingblocks and flow diagram blocks and sequences are thus within the scopeand spirit of the claims. One of average skill in the art will alsorecognize that the functional building blocks, and other illustrativeblocks, modules and components herein, can be implemented as illustratedor by discrete components, application specific integrated circuits,processors executing appropriate software and the like or anycombination thereof

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with one or more other routines. In addition, a flow diagrammay include an “end” and/or “continue” indication. The “end” and/or“continue” indications reflect that the steps presented can end asdescribed and shown or optionally be incorporated in or otherwise usedin conjunction with one or more other routines. In this context, “start”indicates the beginning of the first step presented and may be precededby other activities not specifically shown. Further, the “continue”indication reflects that the steps presented may be performed multipletimes and/or may be succeeded by other activities not specificallyshown. Further, while a flow diagram indicates a particular ordering ofsteps, other orderings are likewise possible provided that theprinciples of causality are maintained.

The one or more embodiments are used herein to illustrate one or moreaspects, one or more features, one or more concepts, and/or one or moreexamples. A physical embodiment of an apparatus, an article ofmanufacture, a machine, and/or of a process may include one or more ofthe aspects, features, concepts, examples, etc. described with referenceto one or more of the embodiments discussed herein. Further, from figureto figure, the embodiments may incorporate the same or similarly namedfunctions, steps, modules, etc. that may use the same or differentreference numbers and, as such, the functions, steps, modules, etc. maybe the same or similar functions, steps, modules, etc. or differentones.

Unless specifically stated to the contra, signals to, from, and/orbetween elements in a figure of any of the figures presented herein maybe analog or digital, continuous time or discrete time, and single-endedor differential. For instance, if a signal path is shown as asingle-ended path, it also represents a differential signal path.Similarly, if a signal path is shown as a differential path, it alsorepresents a single-ended signal path. While one or more particulararchitectures are described herein, other architectures can likewise beimplemented that use one or more data buses not expressly shown, directconnectivity between elements, and/or indirect coupling between otherelements as recognized by one of average skill in the art.

The term “module” is used in the description of one or more of theembodiments. A module implements one or more functions via a device suchas a processor or other processing device or other hardware that mayinclude or operate in association with a memory that stores operationalinstructions. A module may operate independently and/or in conjunctionwith software and/or firmware. As also used herein, a module may containone or more sub-modules, each of which may be one or more modules.

As may further be used herein, a computer readable memory includes oneor more memory elements. A memory element may be a separate memorydevice, multiple memory devices, or a set of memory locations within amemory device. Such a memory device may be a read-only memory, randomaccess memory, volatile memory, non-volatile memory, static memory,dynamic memory, flash memory, cache memory, a quantum register or otherquantum memory and/or any other device that stores data in anon-transitory manner. Furthermore, the memory device may be in a formof a solid-state memory, a hard drive memory or other disk storage,cloud memory, thumb drive, server memory, computing device memory,and/or other non-transitory medium for storing data. The storage of dataincludes temporary storage (i.e., data is lost when power is removedfrom the memory element) and/or persistent storage (i.e., data isretained when power is removed from the memory element). As used herein,a transitory medium shall mean one or more of: (a) a wired or wirelessmedium for the transportation of data as a signal from one computingdevice to another computing device for temporary storage or persistentstorage; (b) a wired or wireless medium for the transportation of dataas a signal within a computing device from one element of the computingdevice to another element of the computing device for temporary storageor persistent storage; (c) a wired or wireless medium for thetransportation of data as a signal from one computing device to anothercomputing device for processing the data by the other computing device;and (d) a wired or wireless medium for the transportation of data as asignal within a computing device from one element of the computingdevice to another element of the computing device for processing thedata by the other element of the computing device. As may be usedherein, a non-transitory computer readable memory is substantiallyequivalent to a computer readable memory. A non-transitory computerreadable memory can also be referred to as a non-transitory computerreadable storage medium.

While particular combinations of various functions and features of theone or more embodiments have been expressly described herein, othercombinations of these features and functions are likewise possible. Thepresent disclosure is not limited by the particular examples disclosedherein and expressly incorporates these other combinations.

What is claimed is:
 1. A vehicle transaction support system, comprising:at least one processing system that includes a processor; and at leastone memory including a non-transitory computer readable storage mediumthat stores executable instructions that, when executed by the at leastone processing system, facilitate performance of operations thatinclude: generating, based on customer prequalification data andextended bureau data and utilizing a customer grading engine having afirst artificial intelligence model trained via machine learning, acustomer grade that predicts a likelihood of transaction defaultassociated with a customer; generating, based on customer profile datathat includes the customer grade and other customer profile data andutilizing a vehicle recommendation engine having a second artificialintelligence model trained via machine learning, vehicle options datathat indicates vehicles selected from a vehicle database; andgenerating, based on at least a portion of the customer profile data andthe vehicle options data and utilizing a transaction recommendationengine having a third artificial intelligence model trained via machinelearning, terms data associated with each of the vehicles selected fromthe vehicle database.
 2. The vehicle transaction support system of claim1, wherein the operations further include: receiving customer input datagenerated via customer interaction with a graphical user interface; andgenerating the customer prequalification data and the other customerprofile data from the customer input data.
 3. The vehicle transactionsupport system of claim 1, wherein the extended bureau data includescustomer data received from a credit bureau that does not include acredit score.
 4. The vehicle transaction support system of claim 1,wherein the operations further include: sending the vehicle options dataand terms data for display via a graphical user interface of a mobiledevice.
 5. The vehicle transaction support system of claim 4, whereinthe operations further include: obtaining vehicle selection data andupdated terms data generated via customer interaction with the graphicaluser interface; and comparing the updated terms data to term limitsdata; generating transaction feedback data indicating acceptance of theupdated terms data for display via the graphical user interface, whenthe updated terms data compare favorably to the term limits data; andgenerating transaction feedback data indicating rejection of the updatedterms for display via the graphical user interface, when the updatedterms data compare unfavorably to the term limits data.
 6. The vehicletransaction support system of claim 1, wherein the first artificialintelligence model is an ensemble model that includes: a plurality ofauto encoding sub-models configured to process the customerprequalification data and the extended bureau data; and a mastersub-model configured to generate the customer grade by processing thecustomer prequalification data and the extended bureau data togetherwith outputs of the plurality of auto encoding sub-models.
 7. Thevehicle transaction support system of claim 1, wherein the secondartificial intelligence model is an ensemble model that includes: avehicle category selection sub-model configured to generate a selectedvehicle category from a plurality of vehicle categories based on thecustomer profile data; a vehicle parameter selection sub-modelconfigured to generate a plurality of vehicle parameters for theselected vehicle category based on the customer profile data; and avehicle search engine configured to generate the vehicles selected fromthe vehicle database based on a search of the vehicle database forvehicles having vehicle data that compares favorably to the vehicleparameters.
 8. The vehicle transaction support system of claim 1,wherein the third artificial intelligence model is an ensemble modelthat includes: a transaction parameter selection sub-model configured togenerate, based on the at least a portion of the customer profile data,transaction parameters that indicate a collection of deal termspredicted for acceptance; and a terms selection sub-model configured togenerate, based on the transaction parameters and the vehicle optionsdata the terms data associated with each of the vehicles selected fromthe vehicle database.
 9. The vehicle transaction support system of claim8, wherein the terms selection sub-model is an ensemble model thatincludes: a first sub-model trained to generate, based on the at least aportion of the customer profile data, the transaction parameters, andthe vehicle options data, a net present value based on non-defaultingtransactions; a second sub-model trained to generate, based on the atleast a portion of the customer profile data, the transactionparameters, and the vehicle options data, a net present value based ondefaulting transactions; a third sub-model trained to generate, based onthe at least a portion of the customer profile data, the transactionparameters, and the vehicle options data, probability of default; and anet present value function configured to generate the terms dataassociated with each of the vehicles selected from the vehicle database,based on outputs from the first sub-model, the second sub-model and thethird sub-model.
 10. The vehicle transaction support system of claim 1,wherein the operations further include: sending the vehicle options dataand terms data for display via a graphical user interface of a mobiledevice in a customer mode of operation of the mobile device;communicating agent data with the mobile device in an agent mode of themobile device, wherein the agent data is not displayed via the graphicaluser interface of the mobile device in the customer mode of operation.11. A method comprising: generating, based on customer prequalificationdata and extended bureau data and utilizing a customer grading enginehaving a first artificial intelligence model trained via machinelearning, a customer grade that predicts a likelihood of transactiondefault associated with a customer; generating, based on customerprofile data that includes the customer grade and other customer profiledata and utilizing a vehicle recommendation engine having a secondartificial intelligence model trained via machine learning, vehicleoptions data that indicates vehicles selected from a vehicle database;and generating, based on at least a portion of the customer profile dataand the vehicle options data and utilizing a transaction recommendationengine having a third artificial intelligence model trained via machinelearning, terms data associated with each of the vehicles selected fromthe vehicle database.
 12. The method of claim 11, further comprising:receiving customer input data generated via customer interaction with agraphical user interface; and generating the customer prequalificationdata and the other customer profile data from the customer input data.13. The vehicle transaction support system of claim 1, wherein theextended bureau data includes customer data received from a creditbureau that does not include a credit score.
 14. The method of claim 11,further comprising: sending the vehicle options data and terms data fordisplay via a graphical user interface of a mobile device.
 15. Themethod of claim 14, further comprising: obtaining vehicle selection dataand updated terms data generated via customer interaction with thegraphical user interface; comparing the updated terms data to termlimits data; generating transaction feedback data indicating acceptanceof the updated terms data for display via the graphical user interface,when the updated terms data compare favorably to the term limits data;and generating transaction feedback data indicating rejection of theupdated terms for display via the graphical user interface, when theupdated terms data compare unfavorably to the term limits data.
 16. Themethod of claim 11, wherein the first artificial intelligence model isan ensemble model that includes: a plurality of auto encoding sub-modelsconfigured to process the customer prequalification data and theextended bureau data; and a master sub-model configured to generate thecustomer grade by processing the customer prequalification data and theextended bureau data together with outputs of the plurality of autoencoding sub-models.
 17. The method of claim 11, wherein the secondartificial intelligence model is an ensemble model that includes: avehicle category selection sub-model configured to generate a selectedvehicle category from a plurality of vehicle categories based on thecustomer profile data; a vehicle parameter selection sub-modelconfigured to generate a plurality of vehicle parameters for theselected vehicle category based on the customer profile data; and avehicle search engine configured to generate the vehicles selected fromthe vehicle database based on a search of the vehicle database forvehicles having vehicle data that compares favorably to the vehicleparameters.
 18. The method of claim 11, wherein the third artificialintelligence model is an ensemble model that includes: a transactionparameter selection sub-model configured to generate, based on the atleast a portion of the customer profile data, transaction parametersthat indicate a collection of deal terms predicted for acceptance; and aterms selection sub-model configured to generate, based on thetransaction parameters and the vehicle options data the terms dataassociated with each of the vehicles selected from the vehicle database.19. The method of claim 18, wherein the terms selection sub-model is anensemble model that includes: a first sub-model trained to generate,based on the at least a portion of the customer profile data, thetransaction parameters, and the vehicle options data, a net presentvalue based on non-defaulting transactions; a second sub-model trainedto generate, based on the at least a portion of the customer profiledata, the transaction parameters, and the vehicle options data, a netpresent value based on defaulting transactions; a third sub-modeltrained to generate, based on the at least a portion of the customerprofile data, the transaction parameters, and the vehicle options data,probability of default; and a net present value function configured togenerate the terms data associated with each of the vehicles selectedfrom the vehicle database, based on outputs from the first sub-model,the second sub-model and the third sub-model.
 20. The method of claim11, further comprising: sending the vehicle options data and terms datafor display via a graphical user interface of a mobile device in acustomer mode of operation of the mobile device; and communicating agentdata with the mobile device in an agent mode of the mobile device,wherein the agent data is not displayed via the graphical user interfaceof the mobile device in the customer mode of operation.